grind_time_for_dose_cv — DRIVE `doseunif` op

• Data kinds: signal × signal × scalar × scalar → table

• Call: import fullseye as fs; fs.ledger.grind_time_for_dose_cv(t, d63, dose_mg: 'float', density: 'float', target_cv: 'float | None' = None, pass_probability: 'float | None' = None, law: 'str' = 'best', k_accept: 'float' = 2.4, limit_av: 'float' = 15.0, n_units: 'int' = 10, ref_range=(98.5, 101.5), n_mc: 'int' = 20000, seed: 'int' = 0) -> 'dict' (to call the implementation directly, import doseunif; doseunif.grind_time_for_dose_cv(t, d63, dose_mg: 'float', density: 'float', target_cv: 'float | None' = None, pass_probability: 'float | None' = None, law: 'str' = 'best', k_accept: 'float' = 2.4, limit_av: 'float' = 15.0, n_units: 'int' = 10, ref_range=(98.5, 101.5), n_mc: 'int' = 20000, seed: 'int' = 0) -> 'dict'; from the registry, opsdrive.get("grind_time_for_dose_cv"))

Usage

Fits a comminution law to the time course of the moment diameter D63 and back-calculates the grinding time needed for the content CV to fall to the target.

> The detailed description below is the original text — the summary and the headings are translated.

t: 粉砕時間(エネルギーの代わり、grind の約束)、d63: その時点の D63 [µm] (dose_cv_from_sizes の d63。D50 でなく D63 を使うのは、CV が D63 だけで決まり分布の形の仮定が要らないから —— 粉砕則を D63 に当てるのは D50 に当てるのと同じく仮定)。

目標: target_cv を直接、または pass_probability = 10 個の第 1 段に合格する確率(χ² の閉形式、平均のずれを無視した上限)から CV = (L1/k) √((n−1)/χ²_p(n−1)) / 100。どちらも無ければ L1/k/100(= 0.0625、「AV ≤ 15 相当」の目安、合格はおよそ半分)。必要な D63 は (CV² D / c)^{1/3}(Poisson、c = πρ/6)。3 則(kick / bond / rittinger)全部で時間を出し、law="best" は log の rms が最小の則、名前を渡せばその則。則による違いは t_req_by_law と t_req_spread に(模型の不確かさとして読む)。

返り: t_req、law、d63_req、target_cv、t_req_by_law、t_req_spread(min, max)、extrapolated(t_req が測った範囲を超える)・ extrap_ratio、already_met(最初の点で既に目標以下)、cv_observed(各点の CV)、pass_probability_chi2(目標の CV での上限)・pass_probability_mc(平均のずれの項も入れた Monte Carlo、正規近似)、fits(grind.comminution_law_fit の要約)。Raises ValueError: 点が 3 未満・非正・長さ違い、target と pass_probability の両方、範囲外の確率・CV、綴り違い。

References (sample data, literature)

• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).

• Operator provenance and references — the sources of the research/methods this op family came from.

• The canonical algorithm (author, year) and its uses are named in the family usage guide above.

Runnable examples (verified samples that actually call this op)

• poc_dose_uniformity_from_grinding — py -3.11 examples/poc_dose_uniformity_from_grinding.py

Ops the type connects to (they accept `table` as input)

course_layout · course_occupancy · course_contains · world_build · world_camera · world_move · lidar_scan · rss_longitudinal_same

Same category (`doseunif`)

dose_cv_lognormal · dose_cv_from_sizes

*Provenance: doseunif.py — DRIVE operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

grind_time_for_dose_cv — DRIVE doseunif op